Source code for mars.tensor.random.uniform

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import numpy as np

from ... import opcodes as OperandDef
from ...serialize import AnyField
from .core import TensorRandomOperandMixin, handle_array, TensorDistribution


class TensorUniform(TensorDistribution, TensorRandomOperandMixin):
    __slots__ = '_low', '_high', '_size'
    _input_fields_ = ['_low', '_high']
    _op_type_ = OperandDef.RAND_UNIFORM

    _low = AnyField('low')
    _high = AnyField('high')
    _func_name = 'uniform'

    def __init__(self, size=None, state=None, dtype=None, gpu=None, **kw):
        dtype = np.dtype(dtype) if dtype is not None else dtype
        super().__init__(_size=size, _state=state, _dtype=dtype, _gpu=gpu, **kw)

    @property
    def low(self):
        return self._low

    @property
    def high(self):
        return self._high

    def __call__(self, low, high, chunk_size=None):
        return self.new_tensor([low, high], None, raw_chunk_size=chunk_size)


[docs]def uniform(random_state, low=0.0, high=1.0, size=None, chunk_size=None, gpu=None, dtype=None): r""" Draw samples from a uniform distribution. Samples are uniformly distributed over the half-open interval ``[low, high)`` (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by `uniform`. Parameters ---------- low : float or array_like of floats, optional Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0. high : float or array_like of floats Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. If size is ``None`` (default), a single value is returned if ``low`` and ``high`` are both scalars. Otherwise, ``mt.broadcast(low, high).size`` samples are drawn. chunk_size : int or tuple of int or tuple of ints, optional Desired chunk size on each dimension gpu : bool, optional Allocate the tensor on GPU if True, False as default dtype : data-type, optional Data-type of the returned tensor. Returns ------- out : Tensor or scalar Drawn samples from the parameterized uniform distribution. See Also -------- randint : Discrete uniform distribution, yielding integers. random_integers : Discrete uniform distribution over the closed interval ``[low, high]``. random_sample : Floats uniformly distributed over ``[0, 1)``. random : Alias for `random_sample`. rand : Convenience function that accepts dimensions as input, e.g., ``rand(2,2)`` would generate a 2-by-2 array of floats, uniformly distributed over ``[0, 1)``. Notes ----- The probability density function of the uniform distribution is .. math:: p(x) = \frac{1}{b - a} anywhere within the interval ``[a, b)``, and zero elsewhere. When ``high`` == ``low``, values of ``low`` will be returned. If ``high`` < ``low``, the results are officially undefined and may eventually raise an error, i.e. do not rely on this function to behave when passed arguments satisfying that inequality condition. Examples -------- Draw samples from the distribution: >>> import mars.tensor as mt >>> s = mt.random.uniform(-1,0,1000) All values are within the given interval: >>> mt.all(s >= -1).execute() True >>> mt.all(s < 0).execute() True Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s.execute(), 15, normed=True) >>> plt.plot(bins, mt.ones_like(bins).execute(), linewidth=2, color='r') >>> plt.show() """ if dtype is None: dtype = np.random.RandomState().uniform( handle_array(low), handle_array(high), size=(0,)).dtype size = random_state._handle_size(size) op = TensorUniform(size=size, state=random_state.to_numpy(), gpu=gpu, dtype=dtype) return op(low, high, chunk_size=chunk_size)